It is relatively easy to store vast amounts of information; however, the ability to gain an understanding of what that shows is becoming difficult. For example, graphs are one visual technique employed for trying to understand the data and data interdependencies. However, again, these graphs can grow to enormous size in the area of thousands to millions of nodes and node links, thereby reintroducing a problem of viewing and interacting with such large representations. These large graphs need to be reducible not only to a manageable size but also for user understanding.
Existing systems limit the amount of detail available to the user so the user can only visualize a part of the overall system at a time. For example, some call-dependency browsers show only one level of calls at a given time, which scales but causes the user to get lost in the details. Moreover, existing systems require that the user perform undue work to provide a “logical mapping” from the data source to the diagrams so that the diagram removes irrelevant detail. Still other techniques provide non-graphical tabular data. No scalable architectures exist to allow the user to interactively explore a graphical visualization of a large graph and drill into to see details while also not losing sight of the big picture.